Word Sense Disambiguation Method Research Articles

Clustering for Clarity: Improving Word Sense Disambiguation through Multilevel Analysis

In natural language processing, a critical activity known as word sense disambiguation (WSD) seeks to ascertain the precise meaning of an ambiguous wordin context. Traditional methods for WSD frequently involve supervised learning methods and lexical databases like WordNet. However, these methods fallshort in managing word meaning complexity and capturing fine-grained differences. In this paper, for increasing the precision and granularity of word sensedisambiguation we proposed multilevel clustering method that goes deeper in the nested levels as locate groups of linked context words and categorize themaccording to their word meanings. With this method, we can more effectively manage polysemy and homonymy as well as detect minute differences in meaning. An actual investigation of the SemCor corpus demonstrates the performance score of multilevel clustering in WSD. This proposed method successfullyseparated clusters and groups context terms according to how semantically related they are, producing improved disambiguation outcomes. A more detailedknowledge of word senses and their relationships may be obtained thanks to the clustering process, which makes it possible to identify smaller clusters inside larger clusters. The outcomes demonstrate how multilevel clustering may enhance the granularity and accuracy of WSD. Our solution overcomes the drawbacks of conventional approaches and provides a more fine-grained representation of word senses by combining clustering algorithms.

A Word Sense Disambiguation Method Applied to Natural Language Processing for the Portuguese Language

A Word Sense Disambiguation Method Applied to Natural Language Processing for the Portuguese Language

Attention-based Stacked Bidirectional Long Short-term Memory Model for Word Sense Disambiguation

Word sense disambiguation is a basic task in Natural Language Processing which aims to identify the most appropriate sense of ambiguous words in different contexts by applying algorithm models. In this work, we propose a model that uses a stacked bidirectional Long Short-Term Memory neural network and attention mechanism to determine the sense of ambiguous words. First, the stacked bidirectional Long Short-Term Memory is employed for deep embedding-based representation of sentences containing ambiguous words. Then, we utilize the self-attention mechanism to highlight the contextual features of ambiguous words, and then construct the overall semantic representation of sentences. Finally, the sentence semantic representation is applied to the multilayer perception classifier to generate the appropriate category of the ambiguous word sense items. This model is tested on the Semeval-2007 task-17: English lexical samples dataset and using examples of ambiguous words sourced from Oxford, Cambridge, and Collins dictionaries as extra test datasets. The effectiveness of the proposed approach is demonstrated via comparison with existing word sense disambiguation approaches. Our experimental results show that the proposed model outperforms other word sense disambiguation methods in terms of the evaluation metrics (Average Accuracy, Micro F1-Score, Kappa, and Matthews Correlation Coefficient), and exhibits strong interpretability.

Deep contextual disambiguation of homonyms and polysemants

AbstractA new metric method for word sense disambiguation is put forward, being formulated within a phenomenological analogy to the wave function of an observable quantity in quantum mechanics where the actual meaning of a multivalued word (a homonym or a polysemant) is determined by its context. The choice of the actualized sense is determined by the minimal semantic distance between the semantics of possible word senses and that of the context, where the meanings of the word in question and the context follow from their semantic fields based on lexicographic hyperchains. In contrast to the common models, our method accounts for semantic polarity. The formulated method showed good results in disentangling polysemy, which was not possible to achieve within the contextualized embedding approacdh based on bidirectional encoder representations from transformers (BERT).

Evaluation of Domain-Specific Word Vectors for Biomedical Word Sense Disambiguation.

Among medical applications of natural language processing (NLP), word sense disambiguation (WSD) estimates alternative meanings from text around homonyms. Recently developed NLP methods include word vectors that combine easy computability with nuanced semantic representations. Here we explore the utility of simple linear WSD classifiers based on aggregating word vectors from a modern biomedical NLP library in homonym contexts. We evaluated eight WSD tasks that consider literature abstracts as textual contexts. Discriminative performance was measured in held-out annotations as the median area under sensitivity-specificity curves (AUC) across tasks and 200 bootstrap repetitions. We find that classifiers trained on domain-specific vectors outperformed those from a general language model by 4.0 percentage points, and that a preprocessing step of filtering stopwords and punctuation marks enhanced discrimination by another 0.7 points. The best models achieved a median AUC of 0.992 (interquartile range 0.975 - 0.998). These improvements suggest that more advanced WSD methods might also benefit from leveraging domain-specific vectors derived from large biomedical corpora.

A Review on Methods to Solve Polysemy Problem in WSD Occurring while Processing Marathi Text

Word Sense Disambiguation (WSD) is an open problem in computational linguistics concerned with identifying which sense of a word is used in a sentence. It basically resolves the ambiguity that arises when determining the meaning of the same word used in a particular context in different situations. The solution to this issue impacts other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence and inference. Marathi is an official language of Maharashtra, India, and co-official Language of Goa & Union territory of Dadra & Nagar Haveli and Daman & Diu with more than an 83 million Native speaker whereas 12 million speaks Marathi as their second language. However, the language barrier is impeding the information technology revolution’s progress. Thus, there is a requisite for competent measures to carry out natural language processing (NCP) through computer processing so that computers can interact through language like Marathi and handle by users who know regional Language. This paper provides a review of the methods for WSD and present modified evaluation. The methods, algorithms, and techniques of Word Sense Disambiguation are examined in this work. We aim to elaborate on resources that will be useful for working in Marathi Word Sense Disambiguation in this paper.

Biomedical Word Sense Disambiguation Based on Graph Attention Networks

Biomedical words have many semantics. Biomedical word sense disambiguation (WSD) is an important research issue in biomedicine field. Biomedical WSD refers to the process of determining meanings of ambiguous word according to its context. It is widely applied to process, translate and retrieve biomedical texts now. In order to improve WSD accuracy in biomedicine, this paper proposes a new WSD method based on graph attention neural network (GAT). Words, parts of speech, and semantic categories in context of ambiguous word are used as disambiguation features. Disambiguation features and the sentence are used as nodes to construct WSD graph. GAT is used to extract discriminative features, and softmax function is applied to determine semantic category of biomedical ambiguous word. MSH dataset is used to optimize GAT-based WSD classifier and test its accuracy. Experiments show that average accuracy of the proposed method is improved. At the same time, majority voting strategy is adopted to optimize GAT-based WSD classifier further.

Word Sense Disambiguation Using Prior Probability Estimation Based on the Korean WordNet

Supervised disambiguation using a large amount of corpus data delivers better performance than other word sense disambiguation methods. However, it is not easy to construct large-scale, sense-tagged corpora since this requires high cost and time. On the other hand, implementing unsupervised disambiguation is relatively easy, although most of the efforts have not been satisfactory. A primary reason for the performance degradation of unsupervised disambiguation is that the semantic occurrence probability of ambiguous words is not available. Hence, a data deficiency problem occurs while determining the dependency between words. This paper proposes an unsupervised disambiguation method using a prior probability estimation based on the Korean WordNet. This performs better than supervised disambiguation. In the Korean WordNet, all the words have similar semantic characteristics to their related words. Thus, it is assumed that the dependency between words is the same as the dependency between their related words. This resolves the data deficiency problem by determining the dependency between words by calculating the χ2 statistic between related words. Moreover, in order to have the same effect as using the semantic occurrence probability as prior probability, which is used in supervised disambiguation, semantically related words of ambiguous vocabulary are obtained and utilized as prior probability data. An experiment was conducted with Korean, English, and Chinese to evaluate the performance of our proposed lexical disambiguation method. We found that our proposed method had better performance than supervised disambiguation methods even though our method is based on unsupervised disambiguation (using a knowledge-based approach).

Investigating the Feasibility of Deep Learning Methods for Urdu Word Sense Disambiguation

Word Sense Disambiguation (WSD), the process of automatically identifying the correct meaning of a word used in a given context, is a significant challenge in Natural Language Processing. A range of approaches to the problem has been explored by the research community. The majority of these efforts has focused on a relatively small set of languages, particularly English. Research on WSD for South Asian languages, particularly Urdu, is still in its infancy. In recent years, deep learning methods have proved to be extremely successful for a range of Natural Language Processing tasks. The main aim of this study is to apply, evaluate, and compare a range of deep learning methods approaches to Urdu WSD (both Lexical Sample and All-Words) including Simple Recurrent Neural Networks, Long-Short Term Memory, Gated Recurrent Units, Bidirectional Long-Short Term Memory, and Ensemble Learning. The evaluation was carried out on two benchmark corpora: (1) the ULS-WSD-18 corpus and (2) the UAW-WSD-18 corpus. Results (Accuracy = 63.25% and F1-Measure = 0.49) show that a deep learning approach outperforms previously reported results for the Urdu All-Words WSD task, whereas performance using deep learning approaches (Accuracy = 72.63% and F1-Measure = 0.60) are low in comparison to previously reported for the Urdu Lexical Sample task.

Graph Convolutional Network for Word Sense Disambiguation

Word sense disambiguation (WSD) is an important research topic in natural language processing, which is widely applied to text classification, machine translation, and information retrieval. In order to improve disambiguation accuracy, this paper proposes a WSD method based on the graph convolutional network (GCN). Word, part of speech, and semantic category are extracted from contexts of the ambiguous word as discriminative features. Discriminative features and sentence containing the ambiguous word are used as nodes to construct the WSD graph. Word2Vec tool, Doc2Vec tool, pointwise mutual information (PMI), and TF-IDF are applied to compute embeddings of nodes and edge weights. GCN is used to fuse features of a node and its neighbors, and the softmax function is applied to determine the semantic category of the ambiguous word. Training corpus of SemEval-2007: Task #5 is adopted to optimize the proposed WSD classifier. Test corpus of SemEval-2007: Task #5 is used to test the performance of WSD classifier. Experimental results show that average accuracy of the proposed method is improved.

A REVIEW OF WORD SENSE DISAMBIGUATION METHOD

Background: Word Sense Disambiguation (WSD) is known to have a detrimental effect on the precision of information retrieval systems, where WSD is the ability to identify the meanings of words in context. There is a challenge in inference-correct-sensing on ambiguous words. Through many years of research, there have been various solutions to WSD that have been proposed; they have been divided into supervised and knowledge-based unsupervised. Objective: The first objective of this study was to explore the state-of-art of the WSD method with a hybrid method using ontology concepts. Then, with the findings, we may understand which tools are available to build WSD components. The second objective was to determine which method would be the best in giving good performance results of WSD, by analysing how the methods were used to answer specific WSD questions, their production, and how their performance was analysed. Methods: A review of the literature was conducted relating to the performance of WSD research, which used a comparison method of information retrieval analysis. The study compared the types of methods used in case, and examined methods for tools production, tools training, and analysis of performance. Results: In total 12 papers were found that satisfied all 3 inclusion criteria, and there was an anchor paper assigned to be referred. We chose the knowledge-based unsupervised approach because it has fewer word sets constraints than the supervised approaches which require training data. Concept-based ontology will help WSD in finding the semantic words concept with respect to another concept around it. Conclusion: Many methods was explored and compared to determine the most suitable way to build a WSD model based on semantics between words in query texts that can be related to the knowledge concept by using ontological knowledge presentation.

Word Sense Disambiguation Method Based on Graph Model and Word Vector

As a basic research of natural language processing, word sense disambiguation (WSD) has a very important influence on machine translation, classification tasks, retrieval tasks, etc. In order to solve the problem that existing disambiguation methods rely too much on knowledge base, a disambiguation method combining graph model and word vector is proposed in this paper. Firstly, in this method, the text data are preprocessed by removing punctuation marks and segmenting words. Secondly, the dependency relation is extracted by using the tool of PYLTP for dependency parsing, the words of dependency parent node are matched and the undirected graph is built, and the context knowledge of ambiguous words is selected according to the minimum path length set by the graph model. Finally, Word2Vec model is used to train Wikipedia corpus to obtain word vectors containing ambiguous words and contextual knowledge, and calculate the cross similarity of the word vector, the high mean similarity is regarded as the correct meaning of the ambiguous word. The effectiveness of the proposed method is verified by comparative experiments on the SEVAL-2007 Task# 5 dataset.

МОДИФИЦИРОВАННЫЙ МЕТОД УСТРАНЕНИЯ НЕОДНОЗНАЧНОСТИ СМЫСЛА СЛОВ, ОСНОВАННЫЙ НА МЕТОДАХ РАСПРЕДЕЛЕННОГО ПРЕДСТАВЛЕНИЯ

In the text mining tasks, textual representation should be not only efficient but also interpretable,as this enables an understanding of the operational logic underlying the data miningmodels. This paper describes a modified Word Sense Disambiguation (WSD) method which extendstwo well-known variations of the Lesk WSD approach. Given a word and its context, Leskbases its calculations on the overlap between the context of a word and each definition of its senses(gloss) in order to select the proper meaning. The main contribution of the proposed method isthe adoption of the concept of “similarity” between definition and context instead of "overlap", inaddition to expanding the definition with examples provided by WordNet for each sense of thetarget word. The proposed method is also characterized by the use of text similarity measurementfunctions defined in a distributed semantic space. The proposed method has been tested on fivedifferent benchmark datasets for words sense disambiguation tasks and compared with severalbasic methods, including simple Lesk, extended Lesk, WordNet 1st sense, Babelfy and UKB. Theresults show that proposed method outperforms most basic methods with the exception of Babelfyand the WN 1st sense methods.

An unsupervised method for word sense disambiguation

Word sense disambiguation (WSD) finds the actual meaning of a word according to its context. This paper presents a novel WSD method to find the correct sense of a word present in a sentence. The proposed method uses both the WordNet lexical dictionary and the Wikipedia corpus. Initially, we find all the probable senses of the target word using WordNet. For each of the words present in a sense, we calculate the collocation extraction score with the other words in the sentence. The collocation extraction score finds the probability of the occurrence of two words together in the Wikipedia corpus. The maximum collocation extraction score assigns the proper sense for that context of the sentence. Our method is not limited to the bi-grams that are made up of only two consecutive words. Our method can find the probability of having two words together in a sentence when other words separate these two words. To compare our WSD method with current knowledge-based unsupervised and supervised systems, we use different Senseval and SemEval datasets for doing WSD on English words. Finally, the experimental analysis illustrates the significance of the proposed approach over many baseline and current systems.

Disambiguation of Semantic Relations Using Evidence Aggregation According to a Sense Inventory

This paper describes EPROP, a novel technique requiring little prior knowledge for word sense disambiguation of semantic relations between pairs of ambiguous concepts in knowledge bases. Our method makes inferences by aggregating evidences from ambiguous word interpretations and propagating the acquired knowledge over a taxonomy to generalize or specialize this knowledge. This propagation process allows the estimation of the degree of belief for each possible word sense assignment given the available evidence. EPROP only requires a sense inventory structured as a taxonomy to disambiguate a knowledge base by combining evidence from the ambiguous facts stored in the knowledge base. We have performed different experiments that show that our method achieves good results on the disambiguation of the semantic relations included in WordNet and ConceptNet. We also show how our method can be used to improve the performance of state-of-the-art word sense disambiguation methods.

Arabic Gloss WSD Using BERT

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous; 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.

Name Disambiguation Analysis Using the Word Sense Disambiguation Method in Hadith

Name disambiguation is the problem solving process to find similar names in sentences. The ambiguity of names can be found in hadith of Sahih Bukhari, names "Abdullah bin Amru" in hadiths no 27 and “Abdullah bin Amru” in hadith no 58, These names are the same, but there is no proof they are the same person. This problem is the early indication of ambiguity of name in the hadith. Based in this problem, this research aims to find name disambiguation of hadith narrators with classification by considering the perawi chain. To solved this problem the authors used Word Sense Disambiguation (WSD), WSD is a process to assign the same meaning from the sentences, based on the context in which the word appears. To classify several names in the hadith, the authors used KNN algorithm, by combining the WSD and KNN method can reduce the ambiguity of names in hadith. The data used in this study came from the hadith of Sahih Bukhori through the pre-processing stage. After conducting the research showed a collection of hadith numbers with the same name prediction with an accuracy of 99% at k = 1. Thus, this method can be used for name disambiguation.

Machine translation model for effective translation of Hindi poetries into English

ABSTRACT The Word Sense Disambiguation (WSD) is a process of disambiguating the sense of the text according to its context. Machine translation is one of the challenging task since it requires effective representation of the text to capture semantic relation between Hindi lyrics in English normal language behaviour. This paper focuses on WSD methods to deal with dialects that convert Hindi lyrics to English in its syntactic structure of the words. WSD is a phenomenon for disambiguating the text so that machine would be capable to deduce correct sense of individual given words. WSD is critical for solving natural language tasks such as Machine Translation (MT) and speech processing. The distinguishing proof of significant words in Hindi as the language is not as simple as that of dialects in English. The interpretations of sonnets through the machines are exceptionally essential and deliberate about mind-blowing events. The interpretation of English ballads into other local dialects can turn out to be very straightforward, however, vice-versa is troublesome. This is due to the assortment of structures, classes, and feelings of the local dialects. Various endeavours have been connected far and wide towards the programmed interpretation of ballads from local dialects into English. In this paper, we propose a half breed MT (HBMT) procedure driven by the standard based MT together with measurements based on statistical machine translation (SMT) and rule-based machine translation (RBMT) for WSD in natural script Hindi in English Lyrics. This proposed method improves the semantic and syntactic accuracy of a machine interpretation framework. Finally, the proposed approach result is compared with the machine translation methods such a Google and Microsoft Bing Babylonian and HMT translators provided achieves a better outcome compared to the existing standards.

Context expansion approach for graph-based word sense disambiguation

• The study describes new variation of graph-based word senses disambiguation method. • It utilizes all the available semantic information to improve semantic connectivity. • The context expansion allows it to capture a higher degree of semantic information. • Two different model of evaluations on several benchmark datasets are applied. • The results show that the method performs favourably against the existing methods. Word sense disambiguation is a process to correctly identify the meanings of words in a given context. Being important in many natural language processing applications, this process is crucial in automatically understanding natural language expressions. Herein, we propose a variation of a well-known unsupervised graph-based word sense disambiguation method that utilizes all possible semantic information from a used lexical resource to increase graph-semantic connectivity for identifying the intended meanings of words in a given context. If the words have multiple potential meanings (senses) based on context, the proposed method builds an expanded graph representing most relevant semantic information of the words to be disambiguated. Nodes in the graph correspond to the context expansion set, which contains all associated information of each possible meaning of the word (word sense), and edges represent the semantic similarity between the expanded sets (nodes). Simultaneously, actual meaning is assigned to each target word using a locate graph centrality measure, which provides the degree of importance between graph nodes. Unlike most existing graph-based word sense disambiguation methods, wherein semantic relations (edges) between nodes are measured at the word level, the proposed method measures graph node semantic relations at the sentence level by expanding the words’ context, which contains all associated information for each possible word sense. Consequently, the proposed method can capture a higher degree of semantic information than existing approaches, thereby increasing semantic connectivity through a graph’s edges. Empirical results on benchmark datasets demonstrate that the proposed method outperforms all compared state-of-the-art graph-based word sense disambiguation approaches reported herein. We also report results obtained by applying the proposed method to a sentiment analysis task. These results demonstrate that the proposed method can determine the overall sentiment orientation of a given textual context.

Comparing supervised learning algorithms for Spatial Nominal Entity recognition

Abstract. Discourse may contain both named and nominal entities. Most common nouns or nominal mentions in natural language do not have a single, simple meaning but rather a number of related meanings. This form of ambiguity led to the development of a task in natural language processing known as Word Sense Disambiguation. Recognition and categorisation of named and nominal entities is an essential step for Word Sense Disambiguation methods. Up to now, named entity recognition and categorisation systems mainly focused on the annotation, categorisation and identification of named entities. This paper focuses on the annotation and the identification of spatial nominal entities. We explore the combination of Transfer Learning principle and supervised learning algorithms, in order to build a system to detect spatial nominal entities. For this purpose, different supervised learning algorithms are evaluated with three different context sizes on two manually annotated datasets built from Wikipedia articles and hiking description texts. The studied algorithms have been selected for one or more of their specific properties potentially useful in solving our problem. The results of the first phase of experiments reveal that the selected algorithms have similar performances in terms of ability to detect spatial nominal entities. The study also confirms the importance of the size of the window to describe the context, when word-embedding principle is used to represent the semantics of each word.